from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-19 14:07:49.473039
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Sat, 19, Dec, 2020
Time: 14:07:53
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -43.7952
Nobs: 145.000 HQIC: -44.8921
Log likelihood: 1547.39 FPE: 1.50803e-20
AIC: -45.6429 Det(Omega_mle): 8.27439e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.468878 0.170866 2.744 0.006
L1.Burgenland 0.147254 0.083886 1.755 0.079
L1.Kärnten -0.237092 0.067642 -3.505 0.000
L1.Niederösterreich 0.104250 0.200259 0.521 0.603
L1.Oberösterreich 0.242925 0.168168 1.445 0.149
L1.Salzburg 0.176319 0.086577 2.037 0.042
L1.Steiermark 0.086123 0.121400 0.709 0.478
L1.Tirol 0.148343 0.079879 1.857 0.063
L1.Vorarlberg 0.007616 0.077702 0.098 0.922
L1.Wien -0.126432 0.163116 -0.775 0.438
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.555711 0.223338 2.488 0.013
L1.Burgenland 0.013189 0.109647 0.120 0.904
L1.Kärnten 0.362720 0.088414 4.102 0.000
L1.Niederösterreich 0.129188 0.261757 0.494 0.622
L1.Oberösterreich -0.211537 0.219810 -0.962 0.336
L1.Salzburg 0.195621 0.113164 1.729 0.084
L1.Steiermark 0.239085 0.158681 1.507 0.132
L1.Tirol 0.143637 0.104409 1.376 0.169
L1.Vorarlberg 0.188189 0.101564 1.853 0.064
L1.Wien -0.596532 0.213207 -2.798 0.005
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.306684 0.073866 4.152 0.000
L1.Burgenland 0.105219 0.036264 2.901 0.004
L1.Kärnten -0.026139 0.029242 -0.894 0.371
L1.Niederösterreich 0.075390 0.086573 0.871 0.384
L1.Oberösterreich 0.286136 0.072699 3.936 0.000
L1.Salzburg -0.001315 0.037427 -0.035 0.972
L1.Steiermark -0.030490 0.052481 -0.581 0.561
L1.Tirol 0.090370 0.034532 2.617 0.009
L1.Vorarlberg 0.131528 0.033591 3.916 0.000
L1.Wien 0.070089 0.070515 0.994 0.320
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.194053 0.085157 2.279 0.023
L1.Burgenland -0.003934 0.041807 -0.094 0.925
L1.Kärnten 0.020333 0.033712 0.603 0.546
L1.Niederösterreich 0.012913 0.099806 0.129 0.897
L1.Oberösterreich 0.404456 0.083812 4.826 0.000
L1.Salzburg 0.096378 0.043148 2.234 0.026
L1.Steiermark 0.194381 0.060503 3.213 0.001
L1.Tirol 0.031829 0.039810 0.800 0.424
L1.Vorarlberg 0.103113 0.038725 2.663 0.008
L1.Wien -0.054693 0.081294 -0.673 0.501
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.616526 0.179860 3.428 0.001
L1.Burgenland 0.077044 0.088302 0.873 0.383
L1.Kärnten 0.003714 0.071203 0.052 0.958
L1.Niederösterreich -0.067497 0.210800 -0.320 0.749
L1.Oberösterreich 0.133043 0.177019 0.752 0.452
L1.Salzburg 0.043576 0.091134 0.478 0.633
L1.Steiermark 0.121249 0.127790 0.949 0.343
L1.Tirol 0.218869 0.084083 2.603 0.009
L1.Vorarlberg 0.017358 0.081792 0.212 0.832
L1.Wien -0.145736 0.171702 -0.849 0.396
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.180125 0.124798 1.443 0.149
L1.Burgenland -0.031364 0.061269 -0.512 0.609
L1.Kärnten -0.014864 0.049405 -0.301 0.764
L1.Niederösterreich 0.153847 0.146266 1.052 0.293
L1.Oberösterreich 0.401981 0.122827 3.273 0.001
L1.Salzburg -0.024602 0.063234 -0.389 0.697
L1.Steiermark -0.042436 0.088668 -0.479 0.632
L1.Tirol 0.188766 0.058342 3.236 0.001
L1.Vorarlberg 0.037191 0.056752 0.655 0.512
L1.Wien 0.161554 0.119137 1.356 0.175
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.201138 0.156599 1.284 0.199
L1.Burgenland 0.083369 0.076882 1.084 0.278
L1.Kärnten -0.044436 0.061994 -0.717 0.474
L1.Niederösterreich -0.034350 0.183538 -0.187 0.852
L1.Oberösterreich -0.122379 0.154126 -0.794 0.427
L1.Salzburg 0.009788 0.079348 0.123 0.902
L1.Steiermark 0.388764 0.111263 3.494 0.000
L1.Tirol 0.518295 0.073209 7.080 0.000
L1.Vorarlberg 0.224981 0.071214 3.159 0.002
L1.Wien -0.223243 0.149496 -1.493 0.135
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.100024 0.181518 0.551 0.582
L1.Burgenland 0.032373 0.089116 0.363 0.716
L1.Kärnten -0.115863 0.071859 -1.612 0.107
L1.Niederösterreich 0.173994 0.212743 0.818 0.413
L1.Oberösterreich 0.016669 0.178651 0.093 0.926
L1.Salzburg 0.225096 0.091974 2.447 0.014
L1.Steiermark 0.152244 0.128968 1.180 0.238
L1.Tirol 0.088596 0.084858 1.044 0.296
L1.Vorarlberg 0.038422 0.082546 0.465 0.642
L1.Wien 0.299185 0.173284 1.727 0.084
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.583143 0.100704 5.791 0.000
L1.Burgenland -0.014380 0.049440 -0.291 0.771
L1.Kärnten -0.000842 0.039866 -0.021 0.983
L1.Niederösterreich -0.032173 0.118027 -0.273 0.785
L1.Oberösterreich 0.279689 0.099113 2.822 0.005
L1.Salzburg 0.009188 0.051026 0.180 0.857
L1.Steiermark 0.009789 0.071549 0.137 0.891
L1.Tirol 0.077041 0.047078 1.636 0.102
L1.Vorarlberg 0.180801 0.045795 3.948 0.000
L1.Wien -0.086626 0.096136 -0.901 0.368
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.130035 -0.017249 0.186934 0.239128 0.032312 0.082638 -0.122297 0.144499
Kärnten 0.130035 1.000000 -0.026765 0.179363 0.124717 -0.160884 0.167919 0.020254 0.295767
Niederösterreich -0.017249 -0.026765 1.000000 0.252800 0.059223 0.196562 0.084144 0.024255 0.350597
Oberösterreich 0.186934 0.179363 0.252800 1.000000 0.269645 0.274729 0.078551 0.050611 0.068784
Salzburg 0.239128 0.124717 0.059223 0.269645 1.000000 0.139359 0.059625 0.069626 -0.040095
Steiermark 0.032312 -0.160884 0.196562 0.274729 0.139359 1.000000 0.094056 0.066766 -0.166109
Tirol 0.082638 0.167919 0.084144 0.078551 0.059625 0.094056 1.000000 0.128392 0.109938
Vorarlberg -0.122297 0.020254 0.024255 0.050611 0.069626 0.066766 0.128392 1.000000 0.073509
Wien 0.144499 0.295767 0.350597 0.068784 -0.040095 -0.166109 0.109938 0.073509 1.000000